Project Summary/ Abstract. The breast cancer health disparity between women of African ancestry (AA) and European ancestry (EA) remains a huge public health challenge in the US. AA women are afflicted by a high rate of the triple-negative breast cancer (TNBC), and bear the highest mortality rate of all populations from the disease. Even within TNBC, some data suggest AA women fare worse than EA women. Mounting evidence points to possible population differences in cancer biology, a fundamental question that remains unsettled. The disparity also manifests in representativeness in tumor genomic sequencing projects. The existing data of breast tumor mutations are dominated by cases from EA populations, which may not represent AA cancer genomes. Moreover, as discovery potential for new driver genes has come close to a plateau, cancer genomes in AAs raised in distinct genetic and environmental context may provide a powerful venue for uncovering mutations that are rare in EA cases. This is clearly showcased in recent studies in other cancer types. Thus, we propose here a study to characterize the mutational landscape of AA breast cancer genomes by pooling resources from five studies, which will create the largest AA tumor mutation dataset. We hypothesize that endogenous and exogenous exposures leave characteristic mutational signatures on cancer genome, providing a trackable historic record of cancer etiology and heterogeneity. Thus, we will investigate etiological links of tumor mutations with genetic and environmental factors by leveraging the available rich epidemiologic and genotype data resources. We have four Specific Aims. First, we will characterize mutational landscape of TNBC in AA women by performing whole-exome sequencing and RNA-sequencing of 500 tumors. We will identify and compare significantly mutated genes and mutational signatures in AA TNBC cases with EA cases from public data sources, to test whether there are population-level differences. Second, based on data from Aim 1 and published literature, we will assemble a targeted gene panel and sequence an additional 2,500 AA tumors, inclusive of all subtypes. The design will cover all genes included in B-CAST, an ongoing breast tumor sequencing project of EA cases in Europe. Data generated in Aim 2 will be used to validate SMGs identified in Aim 1, and to further assess population-level mutational differences in comparison to EA data from B-CAST and others across all cancer subtypes. Third, we will examine etiological links between hormone-related risk factors for breast cancer and somatic mutations. Fourth, we will examine genetic ancestry and genetic variants with tumor mutations. On the basis of the data above, we will identify breast cancer etiological subtypes using an integrative analytical approach. The proposed work will greatly advance the field of breast cancer research by characterizing tumor mutational landscape in AA populations and determining whether cancer biology at the somatic mutation level differs by ancestral population. The findings may have translational significance by revealing cancer causation and providing new targets and motivations for cancer preventive initiatives.